The background description provided herein is for the purpose of generally presenting the context of the disclosure. Unless otherwise indicated herein, the materials described in this section are not prior art to the claims in this application and are not admitted to be prior art by inclusion in this section.
By leveraging cloud computing service-models, service providers deploy and maintain multiple workloads across globally distributed data centers. Current approaches to workload scheduling and placement sacrifice either precision or scalability when presented with heterogeneous platform configurations, which limits the value of specialization in the infrastructure. In a heterogeneous environment, the challenge for the service provider is to enable the infrastructure resource manager (also referred to as the “orchestrator”) to properly decide amongst multiple placement options, the best-match in terms of compute, network and storage resources for instantiating the different components of a workload whilst meeting the SLA (Service Level Agreement). Another key challenge is the trade-off between the business objectives of the service customers who seeks best service performance vs the service provider who seeks to optimize the data center for total cost of ownership (TCO).
However, currently, there is no unified methodology for the calculation of the preference of a placement/rebalancing solution and its comparison from another. Thus, the technical problems that need to be solved to improve the operation of cloud computing infrastructure, in particular, the management of cloud computing infrastructure resources, are:
a) how to effectively quantify the parameters affecting placements/rebalancing of the cloud computing infrastructure resources;
b) how an orchestrator could efficiently compare and contrast potential placement/rebalancing solution; and
c) how the orchestrator can automatically and at scale reason and execute placement decisions to optimally deliver value from differentiating platform features.